The Role of the Data Scientist is in High Demand. But What Does it Take to Fill the Job?

As businesses rely increasingly on data to help
them become more intelligent enterprises, the roles that involve data skills
have become ever more sought-after. I recently attended the annual meeting of
the Society for Industrial/Organizational Psychology (SIOP) in Washington, D.C.
There was a notable number of sessions and tracks this year solely focused on “Data
Science” and blending the worlds of Computer Science and Social Science
together. Industrial/Organizational Psychologists, the discipline within which
I was trained, are taught to leverage research methods and advanced statistics
to better understand people at work and leverage those insights to improve
workplaces. This has been the focus of our science since the days of Hugo
Munsterberg at the turn of the 19th Century. Today with
unprecedented computing power and advanced analytic techniques, IO’s are
leading the wave of HR Analytics and in many companies involved in broader data
science applications to better understand consumer behavior and business
performance.

It was apparent as I sat in sessions talking about honing one’s skills in Python, R using resources like SWIRL and Coursera, that the future is bright and the learning is rather “democratized” for data scientists. Many of the professionals extended their graduate training through self-discipline to learn more about the methods that underlie deep learning, machine learning, and the management of truly “Big Data.” It was also clear that for people with the right skills, a career as a data scientist could be a potentially lucrative move. So, what are those skills that a data scientist position requires? And what should organizations themselves be doing to increase the data knowledge wealth within their organization?

The Traits of a Successful Data Scientist

The World Economic Forum’s 2018 Future of Jobs Report surveyed more than
300 of the world’s largest companies and 85 percent said they intended to
expand their use of big data analytics by 2022.

First and foremost, data scientists must be
problem solvers. They need to be curious, understand what data to look for, how to find it and how
to extract meaning from it. One of the fundamental differences between
traditional, hypothesis-based science and data science is that our computing
power today allows us to extract trends from data that may tell us a story,
rather than having a focused question and line of inquiry to test and
understand a phenomenon. From a technical standpoint, data scientists need a
grounding in math or statistics, IT or coding, or at least have people around
them who can code. It is amazing how many resources have developed in the last
five years to provide learning that is either free-of-charge or relatively
inexpensive for those that are curious and have basic foundations from which to
learn data science methods and tools.

Data scientists will therefore need to be
able to interpret what various data sets mean for the business, and be able to
communicate those findings to senior, probably non-technically-minded decision
makers. How will these data sets make the enterprise more efficient, productive,
secure, compliant and – ultimately – more profitable.

Training is Key to Enterprise-wide Success

One of the responsibilities of a data
scientist will be to make sure that data skills are nurtured across the
enterprise. According to the World Economic Forum, by 2022 just over half (54%)
of employees will require significant retraining and upskilling, including
analytical thinking.

The data scientist will be critical in driving the overall success of the enterprise, and in the U.S., we can expect some of those profits being ploughed back into using data to help further improvements in hiring, training, product development, marketing and more. Forrester Research analyst Brandon Purcell said demand for data scientists will only continue to grow.

A career in data science is not only one of the most in-demand opportunities, it is also a rewarding position that helps inform the most important decisions an enterprise can make.

About the Author

Rich Cober is MicroStrategy’s Chief Human Resources Officer. MicroStrategy is a leading BI platform. In this role, Rich leads MicroStrategy’s teams responsible for HR Business Partnership, HR Operations, Talent Acquisition, and Talent Management. Prior to this role, Rich worked for Marriott International where he served as a senior HR Business Partner, senior leader within Marriott’s learning organization, and a senior leader responsible for Marriott’s Talent Management Analytics and Solutions (TMAS) Team. Rich began his career earning his Ph.D. in Industrial/Organizational Psychology from the University of Akron, was a visiting professor in Cleveland State University’s School of Business and then spent several years working in management consulting.

Resource Links:

Industry Perspectives

In this special guest feature, Brian D’alessandro, Director of Data Science at SparkBeyond, discusses how AI is a learning curve, and exploring opportunities within the technology further extends its potential to enable transformation and generate impact. It can shape workflows to drive efficiency and growth opportunities, while automating other workflows and create new business models. While AI empowers us with the ability to predict the future — we have the opportunity to change it. [READ MORE…]

Latest Video

White Papers

AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on. When things go wrong with AI it’s because either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained. Download the new whitepaper from Alegion that can help AI project leads and business sponsors better understand the four distinct types of bias that can affect machine learning, and how each can be mitigated.